Hardware double buffering using a special purpose computational unit

ABSTRACT

Methods, systems, and apparatus, including an apparatus for transferring data using multiple buffers, including multiple memories and one or more processing units configured to determine buffer memory addresses for a sequence of data elements stored in a first data storage location that are being transferred to a second data storage location. For each group of one or more of the data elements in the sequence, a value of a buffer assignment element that can be switched between multiple values each corresponding to a different one of the memories is identified. A buffer memory address for the group of one or more data elements is determined based on the value of the buffer assignment element. The value of the buffer assignment element is switched prior to determining the buffer memory address for a subsequent group of one or more data elements of the sequence of data elements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of, and claims priorityto, U.S. patent application Ser. No. 15/641,824, now patent Ser. No.10/175,912, titled “HARDWARE DOUBLE BUFFERING USING A SPECIAL PURPOSECOMPUTATIONAL UNIT,” filed on Jul. 5, 2017. The disclosure of theforegoing application is incorporated herein by reference in itsentirety for all purposes.

BACKGROUND

This specification generally relates to performing machine learningcomputations using a special purpose computational unit with hardwaredouble buffers.

Neural networks are machine learning models that employ one or morelayers of models to generate an output, e.g., a classification, for areceived input. Some neural networks include one or more hidden layersin addition to an outer layer. The output of each hidden layer is usedas input to the next layer in the network, i.e., the next hidden layeror the output layer of the network. Each layer of the network generatesan output from a received input in accordance with current values of arespective set of parameters.

Some neural networks include one or more convolutional neural networklayers. Each convolutional neural network layer has an associated set ofkernels. Kernels can be represented as a matrix structure of weightinputs. Each convolutional layer uses the kernels to process inputs tothe layer. A set of inputs to the layer can also be represented as amatrix structure.

SUMMARY

This specification describes technologies relating to using a specialpurpose computational unit for double buffering data of an N-dimensionaltensor.

In general, one innovative aspect of the subject matter described inthis specification can be embodied in an apparatus for transferringdata. The apparatus can include multiple memories including at least afirst memory and a second memory. The apparatus can also include one ormore processing units. The one or more processing units can beconfigured to determine buffer memory addresses for a sequence of dataelements stored in a first data storage location that are beingtransferred to a second data storage location. For each group of one ormore of the data elements in the sequence, the one or more processingunits can identify a value of a buffer assignment element that can beswitched between multiple values each corresponding to a different oneof the memories. The one or more processing units can determine a buffermemory address for the group of one or more data elements based on thevalue of the buffer assignment element. When the value of the bufferassignment element is a first value corresponding to the first memory,the one or more processing units can assign the group of one or moredata elements to the first memory by determining the buffer memoryaddress for the group of one or more data elements based on acombination of a base address for the first memory and a memory offsetvalue for the group of one or more data elements. When the value of thebuffer assignment element is a second value different from the firstvalue and corresponding to the second memory, the one or more processingunits can assign the data element to the second memory by determiningthe memory address for the group of one or more data elements based on acombination of the base address for the first memory, the memory addressoffset value for the second memory and the memory offset value for thegroup of one or more data elements to assign the group of one or moredata elements to the second memory. The one or more processing units canswitch the value of the buffer assignment element prior to determiningthe buffer memory address for a subsequent group of one or more dataelements of the sequence of data elements. The one or more processingunits can transfer each data element to a respective memory location ofthe first or second memory using the determined buffer memory addressfor each group of one or more data elements.

These and other implementations can each optionally include one or moreof the following features. In some aspects, the first memory and thesecond memory are buffers that each have a first data storage capacity.The first data storage location and the second data storage location caneach include at least a second data storage capacity that is greaterthan the first data storage capacity.

In some aspects, the first memory and the second memory are buffers thateach have a first data storage capacity. The sequence of data elementscan include an amount of data that exceeds the first data storagecapacity.

In some aspects, determining the buffer memory address for the group ofone or more data elements based on the value of the buffer assignmentelement and a memory address offset value for the second memory caninclude determining the memory offset value for the group of one or moredata elements based on a number of iterations of one or more loops usedto iterate through the sequence of data elements.

In some aspects, determining the buffer memory address for the group ofone or more data elements based on the value of the buffer assignmentelement and a memory address offset value for the second memory caninclude, whenever the value of the buffer assignment element is thesecond value, determining, as the buffer memory address for the group ofone or more data elements, a sum of (i) the base address for the firstmemory, (ii) the memory address offset value for the second memory and(iii) the memory offset value for the group of one or more dataelements. Whenever the value of the buffer assignment element is thefirst value, the buffer memory address for the group of one or more dataelements can be determined based on a sum of (i) the base address forthe first memory and (ii) the offset value for the group of one or moredata elements independent of the memory address value for the secondmemory.

In some aspects, the memory offset value for the group of one or moredata elements is based on a number of iterations of a loop for eachdimension of the N-dimensional tensor. The memory address offset valuefor the second memory can be based on a difference between a memoryaddress of the first memory address of the second memory.

The subject matter described in this specification can be implemented inparticular embodiments so as to realize one or more of the followingadvantages. Using multiple memories allows for quicker and moreefficient transfer of data that exceeds the storage capacity of a singlebuffer. For example, if 8 megabytes (MBs) of data is to be transferredfrom a first data storage location to a second data storage location andthe capacity of a buffer is only 4 MBs, the data can be split betweentwo 4 MB buffers. By using nested loops to determine memory addressesfor multiple buffers, the number of instructions for determining theaddresses can be reduced, resulting in denser encoding, fewer memoryresources used, and/or fewer required memory resources. Using a bufferassignment element (e.g., a one-bit toggle counter) that is switchedbetween values after each buffer memory address determination allows forquicker buffer assignment and reduced instruction count to assign datato multiple buffers. In addition, switching the value of the bufferassignment element rather than determining whether a first buffer isfull before assigning data to a second buffer allows for quickerprocessing and less computational demand placed on a processor. Thereduction in instructions also results in higher performance as theprocessing unit processes fewer instructions to determine the memoryaddresses. Encoding a double buffer instruction in a special purposehardware unit reduces the number of computational cycles a processorwould otherwise perform to assign data to buffers and thereforeincreases processor bandwidth for other computation tasks.

Other implementations of this and other aspects include correspondingsystems, apparatus, and computer programs, configured to perform theactions of the methods, encoded on computer storage devices. A system ofone or more computers can be so configured by virtue of software,firmware, hardware, or a combination of them installed on the systemthat in operation cause the system to perform the actions. One or morecomputer programs can be so configured by virtue of having instructionsthat, when executed by data processing apparatus, cause the apparatus toperform the actions.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other potential features, aspects,and advantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example computation system.

FIG. 2 illustrates example data being transferred using buffers andexample buffer assignment elements.

FIG. 3 is a flow diagram that illustrates an example process fortransferring data using double buffering.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

In general, when a software algorithm processes an N-dimensional tensor,a nested loop may be used. Each loop can be responsible for traversing arespective dimension of the N-dimensional tensor. A multi-dimensionaltensor may be a matrix or a multi-dimensional matrix. For example, a2-dimensional tensor is a matrix, while a 3-dimensional tensor is athree-dimensional matrix made up of multiple two-dimensional matrices.Each dimension of the N-dimensional tensor may include one or moreelements, where each element may store a respective data value. Forexample, a tensor may be a variable in a program, where the variable mayhave three dimensions. The first dimension may have a length of threehundred elements, the second dimension may have a length of a thousandelements, and the third dimension may have a length of twenty elements.Of course, other numbers of elements in each dimension are possible.

Traversing the tensor in a nested loop can include a computation of amemory address value of an element to load or store the correspondingdata value of the element. A for-loop is an example of a nested loop,where three loops tracked by three loop index variables (e.g., i, j, andk) can be nested to traverse through a three-dimensional tensor. In aneural network, a value of an element may be used in one or more dotproduct computations associated with the tensor. For example, the valueof the element may be multiplied with a corresponding parameter orweight. The elements of the tensor may be traversed in order usingnested for-loops to access the element and perform one or morecomputations using the value of the element. Continuing the threedimensional tensor example, an outer for-loop may be used to traversethe loop tracked by variable i, a middle for-loop loop may be used totraverse the loop tracked by variable j, and an inner for-loop may beused to traverse the loop tracked by variable k. In this example, thefirst element accessed may be (i=0, j=0, k=0), the second element may be(i=0, j=0, k=1), and so on.

As described below, a tensor traversal unit can be used to determine thememory address for each element in order using nested loops so that aprocessing unit can access the value of the element and perform the oneor more computations using the value of the element. The values ofweights or parameters can also be accessed similarly using nestedfor-loops. The tensor traversal unit can also be used to determine theaddresses for weights or parameters used in the computations and/or forthe outputs of the computations, which may be used as inputs to a hiddenlayer of the neural network.

Techniques described herein allow for loop nests to be encoded toproduce and consume data using double buffering. For example, datastored in tensor elements may be transferred from one data storagelocation to another data storage location using double bufferingtechniques. In a neural network example, activations determined as anoutput of one hidden layer may be provided as inputs to another hiddenlayer and thus may be transferred from an output location, i.e., amemory location where outputs of neural network layers are stored, to aninput location, i.e., a memory location where inputs to neural networklayers are stored. In another example, data representing the output of acalculation may be transferred from a temporary memory location to amore permanent memory location. In each of these examples, the data maybe transferred more quickly and the data can be ready for subsequentprocessing more quickly using double buffering.

Buffering can be used to collect data for a neural network computationprior to the computation being performed. For example, inputs to aneural network layer may be stored in a particular location forretrieval by a processor that performs the computations. While datastored in the particular location are being used to perform neuralnetwork computations, data for the next machine learning computation canbe moved into the buffers. When the previous neural network computationhas been completed, the data stored in the buffers can be moved to theparticular location for retrieval by the processor.

One or more loops in a loop nest may be used to compute buffer memoryaddresses for tensor elements for which the data is being produced orconsumed using the double buffers. Multiple buffers may be used when theamount of data to be transferred is greater than the storage capacity ofa single buffer. For example, if the amount of data being transferred istwice the storage capacity of a single buffer, the data may be splitbetween two buffers. In this example, a first portion of the data may betransferred to a first buffer and a second portion of the data may betransferred to a second buffer before being transferred to the seconddata storage location. In this way, all of the data can be bufferedprior to being transferred to the second data storage location.

As an example, elements of a three dimensional tensor may represent thefeatures of an image being classified by a neural network. A firstdimension (Z) may represent the width of the image, the second dimension(Y) may represent the height of the image, and the third dimension (X)may represent RGB values for pixels in the image. To classify the image,each RGB value may be multiplied by a filter value of a convolutionallayer to generate an activation map.

A nested loop can be used to determine the memory address for accessingeach RGB value of the tensor. The nested loop can include a loop foreach dimension of the tensor. For example, an outer loop (z) may be usedto traverse the Z dimension (the width of the image), a middle loop (y)may be used to traverse the Y dimension (the height of the image), andan inner loop (x) may be used to traverse the X dimension (the three RGBvalues for each pixel). At each iteration of the inner loop, a memoryaddress is determined for one of the three RGB values for a particularpixel of the image represented by the value of the outer loop z and themiddle loop y. For example, the memory address for the R value of thepixel of the image represented by Z=0 and Y=0, may be determined duringthe first iteration of the inner loop x when z=0 and y=0 (e.g., z=0;y=0; x=0). Similarly, the memory address for the G value of the pixel ofthe image represented by Z=5 and Y=2 may be determined during the thirditeration of the inner loop x when z=5 and y=2 (e.g., z=5; y=2; x=2). Ifthe three RGB values for each pixel of the image exceed the capacity ofa buffer, the data representing the three RGB values for each pixel ofthe image can be split between two or more buffers.

To determine the buffer memory addresses for multiple buffers usingnested loops, a value of a buffer assignment element can be switchedafter (or before) each iteration of a loop used to determine the buffermemory addresses. For example, if two buffers are used and the data isbeing split between the two buffers, the buffer assignment value may beswitched between two values. A first value (e.g., 0) of the bufferassignment element may be used to assign a data element (or a group ofdata elements) to a first buffer and a second value (e.g., 1) of thebuffer assignment element may be used to assign a data element (or agroup of data elements) to the second buffer. When the value of thebuffer assignment element is the first value for an iteration of theloop, the data element corresponding to this iteration of the loop maybe assigned to a buffer memory address of the first buffer. Similarly,when the value of the buffer assignment element is the second value foran iteration of the loop, the data element corresponding to thisiteration of the loop may be assigned to a buffer memory address of thesecond buffer. If three or more buffers are used, the buffer assignmentelement may have three or more values, e.g., a value for each buffer.

FIG. 1 is a block diagram of an example computation system 100. Ingeneral, the computing system 100 processes an input 104 to generate anoutput 116. The computing system 100 may be configured to perform linearalgebra computations, neural network computations, and othercomputations. The input 104 may be any suitable data that can beprocessed by the computing system 100. The computing system 100 includesa processing unit 102, one or more storage mediums 104, and a tensortraversal unit 106.

The processing unit 114 can include one or more processors and/or one ormore finite-state machines (FSM). A processor of the processing unit 114can execute an instruction for accessing a particular element of atensor. When the processor processes such an instruction, the tensortraversal unit 106 determines the memory address of the particularelement of the tensor, such that the processing unit may access thestorage medium(s) 104 to read data representing the value of theparticular element.

For processing units that include a FSM, the FSM can query memoryaddresses for tensor elements from the tensor traversal unit 106. Insome implementations, the FSM 108 continuously queries the tensortraversal unit 106 for address values for particular elements of thetensor. The FSM can then provide the received address values to aprocessor of the processing unit 102 so that the processor can accessthe storage medium(s) 104 to read data representing the value of theparticular element.

For example, a program may include a nested loop and the processing unit102 may execute an instruction to access an element of a two-dimensionalarray variable within the nested loop according to current indexvariable values associated with the nested loop. Based on the currentindex variable values associated with the nested loop, the tensortraversal unit 106 may determine an address offset value that representsan offset from a memory address for a first element of thetwo-dimensional array variable. The processing unit 102 may then access,using the address offset value and from the storage medium 104, theparticular element of the two-dimensional array variable.

The storage medium 104 stores information within the computing system100. In some implementations, the storage medium 104 is a volatilememory unit or units. In some other implementations, the storage medium104 is a non-volatile memory unit or units. The storage medium 104 mayalso be another form of computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. The instructions, when executed by the processing unit102, cause the processing unit 102 to perform one or more tasks.

In general, the tensor traversal unit 106 determines a status associatedwith one or more tensors. In some implementations, the status mayinclude loop bound values, current loop index variable values, partialaddress offset values for determining a memory address value, and/orprogram counter values for handling branch loop bounds. The tensortraversal unit 106 may be implemented as an application-specificintegrated circuit.

The tensor traversal unit 106 can translate tensor indices into memoryaddresses. For example, the tensor traversal unit 106 may translate aset of N-dimensional tensor indices into a one-dimensional addressspace. The tensor traversal unit 106 can perform such translations bymaking a tensor element's memory address a combination (e.g., a linearcombination) of the element's dimension indices.

The tensor traversal unit 106 can include one or more tensor statuselements 122 and a math unit 124. Each of the tensor status elements 122may be a storage element, for example, a register or any suitablestorage circuitry. The tensor status elements 122 can include a bufferassignment element described below. The math unit 124 can include one ormore arithmetic logic units (ALUs) and/or one or more hardware adders.The math unit 124 can be used to compute a memory address or memoryaddress offset value for tensor elements, e.g., based on values storedin the tensor status elements. Example techniques for determining memoryaddresses using a tensor traversal unit are described in U.S. patentapplication Ser. No. 15/335,769 titled “Neural Network Compute Tile” andfiled on Oct. 27, 2016 and U.S. patent application Ser. No. 15/014,265titled “Accessing Data in Multi-Dimensional Tensors” and filed on Feb.3, 2016, the entire contents of which are incorporated herein byreference.

The tensor traversal unit 106 can also be configured to determine memoryaddresses for temporary memory locations, e.g., buffers, fortransferring data from one data storage location to another data storagelocation. For example, the storage medium(s) 104 can include multipledata storage locations, including storage location A 112 and storagelocation B 114. Each storage location may be a range of memory addressesof a common memory unit or different memory units. The storage medium(s)104 can also include multiple temporary memories, including buffer A 116and buffer B 118. The processing unit 102 can transfer data from a firstdata storage location (e.g., storage location A 112) to one or morebuffers (e.g., buffer A 116 and/or buffer B 118) before transferring thedata to a second data storage location (e.g., storage location B 114).

The tensor traversal unit 106 can determine buffer memory addresses fortensor elements for which the data is being produced or consumed usingthe buffer (e.g., double buffers). For example, the tensor traversalunit 106 can translate tensor indices into buffer memory addresses usinga base buffer memory address for the tensor and an address offset foreach tensor element that is based on the tensor indices for the tensorelement, similar to the technique described in U.S. patent applicationSer. No. 15/014,265 titled “Accessing Data in Multi-Dimensional Tensors”and filed on Feb. 3, 2016.

When two or more buffers are used to transfer data, the tensor traversalunit 106 can also use a buffer assignment element to assign each tensorelement or group of tensor elements to one of the buffers. In someimplementations, the tensor traversal unit 106 can assign a group of oneor more tensor elements to one of the buffers by adding an additionaloffset to the buffer memory address when the value of the bufferassignment element is one value and assign a group of one or more tensorelements to a different buffer when the buffer assignment element is adifferent value by not adding the additional offset value to the buffermemory address.

For example, a base memory address may correspond to the first memoryaddress of a first buffer. The first memory address of a second buffermay be offset by a particular number of addresses from the base memoryaddress. In this example, to assign a tensor element to a memory addressof the first buffer, the tensor traversal unit 106 may combine (e.g.,add) the base memory address with a memory offset value for the tensorelement. The memory offset value for the tensor element may bedetermined based on the tensor indices of nested loops used to traversethe tensor, as described in U.S. patent application Ser. No. 15/014,265titled “Accessing Data in Multi-Dimensional Tensors” and filed on Feb.3, 2016.

To assign a tensor element to a memory address of the second buffer, thetensor traversal unit 106 may combine (e.g., add) the base memoryaddress with a memory offset value for the tensor element and a memoryaddress offset value for the second buffer (e.g., the particular numberof addresses from which the first memory address of the second buffer isoffset from the first memory address of the first buffer).

The tensor traversal unit 106 can use the value of the buffer assignmentelement to determine when to assign a tensor element to the secondbuffer and therefore determine the buffer memory address for the tensorelement by combining the memory address offset value for the secondbuffer with the base memory address and the memory offset value for thetensor element. For example, when the value of the buffer assignmentelement is a first value, the tensor traversal unit 106 can assign thetensor element to the first buffer by not combining the memory addressoffset value with the base memory address and the memory offset valuefor the tensor element. When the value of the buffer assignment elementis a second value different from the first value, the tensor traversalunit 106 can assign the tensor element to the second buffer by combiningthe memory address offset value with the base memory address and thememory offset value for the tensor element.

In some implementations, the tensor traversal unit 106 may determine thebuffer memory addresses for a sequence of tensor elements in a sequence,e.g., using nested loops. In this example, the processing unit 102 canrequest, from the tensor traversal unit 106, a buffer memory address fora group of one or more tensor elements for each iteration of aparticular loop, e.g., each iteration of an inner most loop. The tensortraversal unit 106 can determine the memory offset value for a group oftensor elements corresponding to the iteration of the loop based on theloop indices. The tensor traversal unit 106 can also determine whetherto assign the group of tensor elements to the first buffer or the secondbuffer (or additional buffers if more than two) based on the value ofthe buffer assignment element, as described above. The tensor traversalunit 106 can determine the buffer memory address for the group of tensorelements based on the base memory address, the memory offset value forthe group of tensor elements, and, depending on the value of the bufferassignment element, the memory offset value for the second buffer.

After determining the buffer memory address for a group of tensorelements in the sequence, the tensor traversal unit can switch the valueof the buffer assignment element. For example, if there are two buffers,the tensor traversal unit 106 may toggle the value between two valuesafter each buffer memory address determination. In this example, thetensor traversal unit 106 can assign a group of tensor elements to thefirst buffer when the value of the buffer assignment element is zero andassign a group of tensor elements to the second buffer when the value ofthe buffer assignment element is one. For a first buffer memory addressdetermination, the buffer assignment element may have a value of zero.In this example, the tensor traversal unit 106 can assign the firstgroup of tensor elements in the sequence to the first buffer. The tensortraversal unit 106 can then switch the value of the buffer assignmentelement to one. Thus, the tensor traversal unit 106 can assign thesecond group of tensor elements in the sequence to the second buffer.The tensor traversal unit 106 can continue switching the value aftereach buffer memory address determination such that every other group oftensor elements is assigned to the first buffer.

In some implementations, coarse-grained toggling is used such that agroup of tensor elements (e.g., a subtensor of the tensor) is assignedto a buffer for each buffer memory address determination. In someimplementations, fine-grained toggling is used such that each individualtensor element is assigned to a buffer at each memory addressdetermination.

Consider an example in which the tensor traversal unit has two 1kilobyte (kB) buffers and 4 kB of data is to be transferred using thebuffers. An example loop nest can include an outer loop that alternatesbetween the two buffers and an inner loop can be used identify eachportion of data to include in the current buffer. For example, thenested loop can include:

for (i=0; i<4, ++i)

-   -   for (j=0; j=1024; ++j)

In this example, the inner loop “j” is used to identify 1 kB of data toinclude in a buffer and outer loop “i” is used to switch between the twobuffers. For example, when “i” has an odd value, the 1 kB group of dataelements may be assigned to the first buffer. When “i” has an evenvalue, the 1 kB of data may be assigned to the second buffer. Thus, inthis example, the loop nest alternates between the two buffers based onthe value of “i”.

If there are more than two buffers, the tensor traversal unit 106 mayswitch the buffer assignment elements between more than two differentvalues, e.g., a unique value for each buffer. For example, if there arethree buffers, the tensor traversal unit 106 can assign a group oftensor elements to the first buffer when the buffer assignment elementhas a first value; the tensor traversal unit 106 can assign a group oftensor elements to the second buffer when the buffer assignment elementhas a second value; and the tensor traversal unit 106 can assign a groupof tensor elements to the third buffer when the buffer assignmentelement has a third value.

In another example, there may be two buffers each having a storagecapacity of 1 MB and 3 MB of data may need to be transferred through thebuffers. In this example, the first 1 MB can be assigned to a first ofthe two buffers and a second 1 MB can be assigned to a second of the twobuffers. Then, after the first 1 MB is consumed, e.g., by a processor,the third 1 MB can be moved to the first buffer.

In some implementations, the tensor traversal unit 106 can obtain asequence of alternating buffer assignment values rather than switch thevalue of a buffer assignment element after each buffer memory addressdetermination. For example, the sequence of alternating bufferassignment values may be a sequence of alternating zeros and ones. Aftereach memory address determination, the tensor traversal unit 106 canmove to the next value in the sequence and assign the group of tensorelements to an appropriate buffer based on the next value.

FIG. 2 illustrates example data being transferred using buffers andexample buffer assignment elements. In this example, a sequence of eightgroups of data elements, e.g., tensor elements, are being transferredfrom a first data storage location 205 to a second data storage location215 using two buffers. A sequence of buffer assignment values 210 areused to assign each group of data elements to one of the two buffers.For example, if the group of data elements is at a same position in itssequence as a buffer assignment element having a value of zero, thegroup of data elements is assigned to a first buffer. If the group ofdata elements is at a same position in its sequence as a bufferassignment element having a value of one, the group of data elements isassigned to a second buffer different from the first buffer.

Thus, in this example, data element groups “0”, “2”, “4”, and “6” areassigned to the first buffer as the first, third, fifth, and seventhbuffer assignment values are zero. Similarly, data element groups “1”,“3”, “5”, and “7” are assigned to the second buffer as the second,fourth, sixth, and eighth buffer assignment values are one. Thus, twobuffers that each have a storage capacity to store four groups of dataelements can be used to buffer the eight groups of data elements.

FIG. 3 is a flow diagram that illustrates an example process 300 fortransferring data using double buffering. The process 300 may beperformed by a system of one or more computers, e.g., the computingsystem 110 of FIG. 1.

The system identifies a sequence of data elements designated for doublebuffering using a first buffer and a second buffer (302). The sequenceof data elements may be a sequence of tensor elements that aredesignated for double buffering. The tensor elements may be a part of anN-dimensional tensor. For example, a tensor may be traversed usingnested loops where each loop is responsible for traversing a respectivedimension of the N-dimensional tensor.

The sequence of data elements may include all of the tensor elements ofa particular dimension that has been designated for double buffering.For example, a program that includes the nested loops may include codedesignating the loop corresponding to the particular dimension as a loopthat is to be double buffered. In a particular three dimensional tensorexample, the tensor may be traversed using three loops with indices x,y, and z. In this example, a Z dimension of the tensor may be traversedusing an outer loop with index z, a Y dimension of the tensor may betraversed using a middle loop with index y, and an X dimension of thetensor may be traversed using an inner loop index x. The inner loop maybe designated for double buffering to quickly buffer data for a neuralnetwork computation.

The system determines a buffer memory address for each group of dataelements in the sequence (304). Each group can include one or more dataelements. For example, if fine-grained toggling is used, each group caninclude one data element. If coarse-grained toggling is used, each groupcan include multiple data elements, e.g., up to a specified amount ofmemory or specified number of data elements.

In some implementations, the system determines the buffer memoryaddresses one at a time. Continuing the previous example, the system maydetermine a buffer memory address for each iteration of the inner loop xas each iteration of the inner loop corresponds to a particular tensorelement designated for double buffering. The system may determine thebuffer memory addresses for the sequence of data elements usingconstituent operations 306-314.

The system identifies a value of a buffer assignment element for a groupof data elements in the sequence of data elements (306). In someimplementations, as described above, the system can switch the value ofthe buffer assignment element after each buffer memory addressdetermination, e.g., after each iteration of a loop designated fordouble buffering. In this example, the system can identify a currentvalue of the buffer assignment element as the value of the bufferassignment element for this data element. The value of the bufferassignment element is used to assign the group of data elements to anappropriate buffer.

The system determines a buffer memory address offset value for the groupof data elements based on the value of the buffer assignment element anda memory address offset value for the second buffer (308). As describedabove, a base memory address for the buffers may correspond to the firstmemory address of a first buffer. The first memory address of a secondbuffer may be offset by a particular number of addresses from the basememory address. The memory address offset value for the second buffermay be equal to the particular number of addresses.

To determine the buffer memory address offset value for the group ofdata elements, the system determines whether the value of the bufferassignment element is a first value or a second value (or more values ifthere are more than two buffers). If the buffer assignment element isthe first value, the system can assign the group of data elements to thefirst buffer by not using the memory address offset value for the secondbuffer when determining the buffer memory address value for the group ofdata elements. Instead, the system can use a memory offset value for thedata element that is determined based on loop indices of the nestedloops, as described above.

If the buffer assignment element is the second value, the system canassign the group of data elements to the second buffer by combining thememory offset value for the group of data elements with the memoryaddress offset value for the second buffer. For example, the system maydetermine, as the buffer memory address offset value for the group ofdata elements, a sum of the memory offset value for the group of dataelements and the memory address offset value for the second buffer.

In some implementations, the system can compute the buffer memoryaddress offset value for a group of data elements by ANDing the value ofthe buffer assignment element with a value of one and multiplying theresult by the memory address offset value for the second buffer, andadding this result to the memory offset value for the group of dataelements. In this example, if the buffer assignment element has a valueof zero, the buffer memory address offset value for the group of dataelements is equal to the memory offset value for the group of dataelements. If the buffer assignment element has a value of one, thebuffer memory address offset value for the group of data elements has avalue equal to the memory address offset value for the second bufferplus the memory offset value for the group of data elements. In someimplementations, a one-bit toggle counter can be used to determine whichbuffer to use.

The system determines the buffer memory address for the group of dataelements based on a base address for the buffers and the buffer memoryaddress offset value (310). For example, the system can determine thebuffer memory address for the group of data elements by adding the baseaddress for the buffers (e.g., the first memory address for the firstbuffer) to the buffer memory address offset value.

The system determines whether a buffer memory address has beendetermined for each data element in the sequence (312). If not, thesystem switches the value of the buffer assignment element for the nextdata element. In this way, the next data element will be assigned to adifferent buffer than the current data element.

If a buffer memory address has been determined for each data element inthe sequence, the system transfers the data stored in the data elementsto buffers based on the determined buffer memory addresses (314). Thedata may then be transferred from the buffers to a second data storagelocation, e.g., for use in neural network computations.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non transitory program carrier for execution by, or to controlthe operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on an artificiallygenerated propagated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array), an ASIC(application specific integrated circuit), or a GPGPU (General purposegraphics processing unit).

Computers suitable for the execution of a computer program include, byway of example, can be based on general or special purposemicroprocessors or both, or any other kind of central processing unit.Generally, a central processing unit will receive instructions and datafrom a read only memory or a random access memory or both. The essentialelements of a computer are a central processing unit for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments can also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

1. (canceled)
 2. An apparatus for transferring data, the apparatuscomprising: a plurality of memories; and one or more processors arrangedto: determine buffer memory addresses for data of an N-dimensionaltensor stored in a first data storage location that is being transferredto a second data storage location, wherein N is an integer that is equalto or greater than two, the determining comprising: identifying acurrent value of a buffer assignment element that can be switchedbetween a plurality of values each corresponding to a different one ofthe plurality of memories; and assigning a first portion of the data ofthe N-dimensional tensor to the memory corresponding to the currentvalue of the buffer assignment element until the memory corresponding tothe current value of the buffer assignment element is full, including:determining the buffer memory address for each data element of the firstportion of data based on at least on a combination of (i) a base addressfor the plurality of memories, (ii) a memory address offset value forthe memory corresponding to the current value of the buffer assignmentelement, and (iii) a memory offset value for the data element, whereinthe memory offset value for each data element is based on current indexvalues of multiple loops in a loop nest used to traverse theN-dimensional tensor; transfer the first portion of the data of theN-dimensional tensor to a respective memory location of the memorycorresponding to the current value of the buffer assignment elementusing the determined buffer memory address for each data element; andswitch the value of the buffer assignment element prior to determiningbuffer memory addresses for a next portion of the data of theN-dimensional tensor.
 3. The apparatus of claim 2, wherein the one ormore processors are arranged to switch the value of the bufferassignment element in response to determining that the memorycorresponding to the current value of the buffer assignment element isfull.
 4. The apparatus of claim 2, wherein the memory address offsetvalue for one of the memories is zero and the memory address offsetvalue for each other memory is non-zero.
 5. The apparatus of claim 2,wherein: each memory of the plurality of memories are buffers that eachhave a first data storage capacity; and the first data storage locationand the second data storage location each comprise at least a seconddata storage capacity that is greater than the first data storagecapacity.
 6. The apparatus of claim 2, wherein the memory offset valuefor each data element is based on a number of iterations of a loop foreach dimension of the N-dimensional tensor.
 7. The apparatus of claim 2,wherein the one or more processors are arranged to transfer the firstportion of the data N-dimensional tensor from the respective memorylocation of the memory corresponding to the current value of the bufferassignment element to the second data storage location.
 8. The apparatusof claim 2, wherein: the value of the buffer assignment element is aloop variable for a loop used to switch the value of the bufferassignment element; and switching the value of the buffer assignmentelement prior to determining buffer memory addresses for a next portionof the data of the N-dimensional tensor comprises iterating the loopvariable in response to determining that the memory corresponding to thecurrent value of the buffer assignment element is full.
 9. A methodperformed by a computing system for transferring data, the methodcomprising: determining buffer memory addresses for data of anN-dimensional tensor stored in a first data storage location that isbeing transferred to a second data storage location, wherein N is aninteger that is equal to or greater than two, the determiningcomprising: identifying a current value of a buffer assignment elementthat can be switched between a plurality of values each corresponding toa different one of a plurality of memories; and assigning a firstportion of the data of the N-dimensional tensor to the memorycorresponding to the current value of the buffer assignment elementuntil the memory corresponding to the current value of the bufferassignment element is full, including: determining the buffer memoryaddress for each data element of the first portion of data based on atleast on a combination of (i) a base address for the plurality ofmemories, (ii) a memory address offset value for the memorycorresponding to the current value of the buffer assignment element, and(iii) a memory offset value for the data element, wherein the memoryoffset value for each data element is based on current index values ofmultiple loops in a loop nest used to traverse the N-dimensional tensor;transferring the first portion of the data of the N-dimensional tensorto a respective memory location of the memory corresponding to thecurrent value of the buffer assignment element using the determinedbuffer memory address for each data element; and switching the value ofthe buffer assignment element prior to determining buffer memoryaddresses for a next portion of the data of the N-dimensional tensor.10. The method of claim 9, wherein the value of the buffer assignmentelement is switched in response to determining that the memorycorresponding to the current value of the buffer assignment element isfull.
 11. The method of claim 9, wherein the memory address offset valuefor one of the memories is zero and the memory address offset value foreach other memory is non-zero.
 12. The method of claim 9, wherein: eachmemory of the plurality of memories are buffers that each have a firstdata storage capacity; and the first data storage location and thesecond data storage location each comprise at least a second datastorage capacity that is greater than the first data storage capacity.13. The method of claim 9, wherein the memory offset value for each dataelement is based on a number of iterations of a loop for each dimensionof the N-dimensional tensor.
 14. The method of claim 9, furthercomprising transferring the first portion of the data N-dimensionaltensor from the respective memory location of the memory correspondingto the current value of the buffer assignment element to the second datastorage location.
 15. The method of claim 9, wherein: the value of thebuffer assignment element is a loop variable for a loop used to switchthe value of the buffer assignment element; and switching the value ofthe buffer assignment element prior to determining buffer memoryaddresses for a next portion of the data of the N-dimensional tensorcomprises iterating the loop variable in response to determining thatthe memory corresponding to the current value of the buffer assignmentelement is full.
 16. A system for transferring data, the systemcomprising: a plurality of memories; and one or more processing unitsthat include one or more math units, the one or more processing unitsconfigured to: determine buffer memory addresses for data of anN-dimensional tensor stored in a first data storage location that isbeing transferred to a second data storage location, wherein N is aninteger that is equal to or greater than two, the determiningcomprising: identifying a current value of a buffer assignment elementthat can be switched between a plurality of values each corresponding toa different one of the plurality of memories; and assigning a firstportion of the data of the N-dimensional tensor to the memorycorresponding to the current value of the buffer assignment elementuntil the memory corresponding to the current value of the bufferassignment element is full, including: determining the buffer memoryaddress for each data element of the first portion of data based on atleast on a combination of (i) a base address for the plurality ofmemories, (ii) a memory address offset value for the memorycorresponding to the current value of the buffer assignment element, and(iii) a memory offset value for the data element, wherein the memoryoffset value for each data element is based on current index values ofmultiple loops in a loop nest used to traverse the N-dimensional tensor;transfer the first portion of the data of the N-dimensional tensor to arespective memory location of the memory corresponding to the currentvalue of the buffer assignment element using the determined buffermemory address for each data element; and switch the value of the bufferassignment element prior to determining buffer memory addresses for anext portion of the data of the N-dimensional tensor.
 17. The system ofclaim 16, wherein the one or more processing units are arranged toswitch the value of the buffer assignment element in response todetermining that the memory corresponding to the current value of thebuffer assignment element is full.
 18. The system of claim 16, whereinthe memory address offset value for one of the memories is zero and thememory address offset value for each other memory is non-zero.
 19. Thesystem of claim 16, wherein: each memory of the plurality of memoriesare buffers that each have a first data storage capacity; and the firstdata storage location and the second data storage location each compriseat least a second data storage capacity that is greater than the firstdata storage capacity.
 20. The system of claim 16, wherein the memoryoffset value for each data element is based on a number of iterations ofa loop for each dimension of the N-dimensional tensor.
 21. The system ofclaim 16, wherein: the value of the buffer assignment element is a loopvariable for a loop used to switch the value of the buffer assignmentelement; and switching the value of the buffer assignment element priorto determining buffer memory addresses for a next portion of the data ofthe N-dimensional tensor comprises iterating the loop variable inresponse to determining that the memory corresponding to the currentvalue of the buffer assignment element is full.